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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fastai\n",
    "import torch\n",
    "import torchvision\n",
    "import os\n",
    "from torchvision import transforms\n",
    "from fastai.vision.all import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#prepare data loader\n",
    "aug_tfm = aug_transforms(\n",
    "                mult=1.5, do_flip=False, p_affine=0.75, pad_mode=\"zeros\"\n",
    "            )\n",
    "data_source = Path(\"./data/generated\")\n",
    "dls = ImageDataLoaders.from_folder(\n",
    "            data_source, valid_pct=0.2, item_tfms=Resize(224), batch_tfms=aug_tfm\n",
    "        )\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#prepare learner on pretrained resnet18 base\n",
    "learn = vision_learner(dls, resnet18, metrics=[error_rate, accuracy])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load pretrained weights\n",
    "learn.load('pretrained')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#saves pretrained weights only (without optimizer state, as pth file)\n",
    "learn.save('pretrained', with_opt=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.fine_tune(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.export('model.pkl')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.13 ('.venv': venv)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "44cab64d7dc7485631db53a8a9ba4dbc7180e4632dd35e38622d9cb1ff9be0a7"
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 },
 "nbformat": 4,
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}